AI Tutorials
ML Fundamentals
Master the full spectrum of machine learning — supervised, unsupervised, reinforcement, ensembles, deep networks, CNNs, RNNs, evaluation metrics, hyperparameter tuning, AutoML, and recommendation systems with hands-on code.
16 chapters · 521 min
Core machine learning algorithms and techniques from first principles
- Ch. 01Read →
Supervised Learning
The complete taxonomy — regression, classification, time series, and probabilistic models
beginner · 45 min
- Ch. 02Read →
Unsupervised Learning
Clustering, dimensionality reduction, and density estimation on unlabeled data
beginner · 28 min
- Ch. 03Read →
Reinforcement Learning
Agents, environments, rewards, and the algorithms that learn through trial and error
intermediate · 28 min
- Ch. 04Read →
Semi-Supervised & Self-Supervised Learning
Learning from limited labels and from data structure itself
intermediate · 22 min
- Ch. 05Read →
Ensemble Techniques
Bagging, boosting, stacking, and why combining models beats any single learner
intermediate · 32 min
- Ch. 06Read →
Deep Neural Networks
Perceptrons, backpropagation, activation functions, and training deep networks
intermediate · 30 min
- Ch. 07Read →
Convolutional Neural Networks (CNNs)
Spatial feature extraction, pooling, and the architectures behind modern computer vision
intermediate · 28 min
- Ch. 08Read →
RNN, LSTM & GRU
Recurrent architectures for sequential data: text, time series, and speech
intermediate · 28 min
- Ch. 09Read →
Model Evaluation Metrics & Techniques
Classification, regression, clustering, and ranking metrics — plus cross-validation techniques
beginner · 38 min
- Ch. 10Read →
Important Hyperparameters
A systematic guide to tuning learning rate, regularization, architecture, and search strategies
intermediate · 25 min
- Ch. 11Read →
EDA & AutoML
Exploratory Data Analysis, feature engineering, dimensionality reduction, and AutoML
beginner · 36 min
- Ch. 12Read →
Recommendation Systems
Collaborative filtering, matrix factorization, content-based, and deep learning approaches
intermediate · 30 min
- Ch. 13Read →
Time Series Forecasting
ARIMA, Prophet, and Temporal Fusion Transformer for demand forecasting and anomaly detection
intermediate · 35 min
- Ch. 14Read →
Graph Neural Networks (GNNs)
GCN, GraphSAGE, and GAT — deep learning on graph-structured data for fraud detection, drug discovery, and recommendations
advanced · 38 min
- Ch. 15Read →
Causal Inference
Counterfactuals, do-calculus, A/B testing, and propensity scoring for data-driven decision making
advanced · 40 min
- Ch. 16Read →
Bayesian Machine Learning
Gaussian Processes, Bayesian optimization, and prior/posterior reasoning for uncertainty quantification
advanced · 38 min